Ocular Diseases Detection Using Machine Learning, Deep Learning and Artificial Intelligence Based Techniques

Abstract

Glaucoma is one the most common and rapidly increasing eye disease. Glaucoma is a condition which affects the retina and is the most common reason for blindness. Glaucoma cannot be detected in its initial stages as it does not show any of its symptoms. Glaucoma was estimated to affect 60 million people in 2010. In 2020,the glaucoma disease affects around seventy-six million people more, which is expected to rise to 111.8 million by 2040. Early diagnosis and treatment is necessary. Along with expert doctors and health professionals, computer aided techniques will be more useful for early and accurate diagnosis and certainly a great help for the medical professionals.Hence, there are many techniques such as deep learning, machine learning and artificial intelligence techniques to detect glaucoma. For glaucoma classification and identificationthereare different deep learning modelsthat have been reviewed in this work which are Inception-V3, Vgg-16, ECNET, Convolution Neural Network (CNN), Deep-Belief Network, EffcientNet and UNet++ models. Machine learning models have also been reviewed in this work for glaucoma diagnosis which are LSSVM(Least Square-Support Vector Machine), XGboost model, Fundus and OCT, SVM.To the best of our knowledge, this is the only comprehensive study which encapsulates various computer-vision based techniques for glaucoma disease detection.